Lightweight Multiscale Spatial Convolution:
For scalable, efficient, and real-time object detection across scales, EfficientDet  [1]} introduced a bidirectional feature pyramid network (FPN) to aggregate features at different resolutions with two-way information flow. The feature network topology is optimized through NAS via heuristic compound scaling of weight, depth, and resolution. EfficientDet is 4–9\(\times \)  smaller, uses 13-42\(\times \)  fewer FLOPS, and outperforms (in terms of latency and mean average precision) YOLOv3, RetinaNet, AmoebaNet, Resnet, and DeepLabV3. Scaled-YOLOv4 [2]} converts portions of FPN of YOLOv4 [3]} to cross-stage partial networks [4]}, which saves up-to 50% computational budget over vanilla CNN backbones. Removal or folding of batch normalization layers and downscaling input resolution can speed up multi-resolution inference by 3.6-8.8\(\times \)  [5]} over vanilla YOLO [6]} or MobileNetsV1 [7]}
